Communication system of digital ward

Abstract

The invention belongs to the technical field of Internet of Things communication, and discloses a communication system of a digital ward. Comprising a distributed acquisition module, an interference identification module, a scattering analysis module, a shielding identification module, a channel map construction module, a communication scheduling module, a relay bridging module, a link monitoring module and a self-adaptive updating module. By sensing an electromagnetic environment of a ward area, an interference aggregation area is identified, multipath scattering characteristics are analyzed, an electromagnetic shielding transition area is found, and then a space-time channel availability map is constructed. And based on the map, generating a frequency hopping time slot interleaving communication scheduling scheme, and establishing a preventive relay bridging link at the boundary of the shielding area. Meanwhile, the communication quality is monitored in real time, incremental updating is triggered when environment change is detected, and the adaptability of the system to the environment is kept. According to the invention, fine management and adaptive scheduling of communication resources in a medical scene are realized, the spectrum utilization efficiency is improved, the anti-interference capability is enhanced, and the monitoring blind area is eliminated.

Description

Technical Field

[0001] This application relates to the field of Internet of Things (IoT) communication technology, and more specifically, to a communication system for a digital ward. Background Technology

[0002] Existing digital ward communication systems face several challenges in complex electromagnetic environments, making it difficult to meet the reliable communication requirements of medical settings. First, co-channel interference is increasingly prominent in hospital ward environments. Numerous medical monitoring devices (such as telemetry ECG monitors, blood oxygen saturation monitors, and smart infusion pumps) and consumer electronics used by patients and their families (smartphones, tablets, and wearable devices) crowd the limited ISM band, creating a highly complex electromagnetic environment. The signals generated by these devices operating simultaneously superimpose and interfere with each other, leading to a severe deterioration in communication channel quality. This is especially true in densely populated ward areas, where data packet collisions and retransmissions occur frequently, jeopardizing the real-time performance and integrity of medical data transmission. Second, the multipath fading effect unique to hospital environments is particularly pronounced. The densely distributed metal equipment (IV stands, medical carts, bed frames), partition walls, and personnel movement within wards cause wireless signals to undergo complex reflections, scattering, and diffraction, resulting in the superposition of multipath signals with different phases at the receiving end. This complex multipath effect leads to deep signal fading, manifested as random discontinuities in communication links and unpredictable drops in signal strength, especially during patient activity or medical staff rounds, potentially causing momentary loss of critical vital signs data during transmission. Third, the electromagnetic shielding blind spots created by the unique architectural structure of hospitals have not been effectively resolved. When patients carrying wireless monitoring devices enter restrooms with metal structures, special examination rooms (such as lead-shielded X-ray rooms), or pass through special treatment areas, the communication link between the monitoring devices and the central station is often completely interrupted, creating dangerous monitoring blind spots. Traditional communication systems lack the ability to sense these shielded areas and preventative switching mechanisms, preventing medical staff from obtaining timely patient status information and creating potential medical risks. Furthermore, existing communication technologies are insufficiently adaptable to the dynamic changes in electromagnetic characteristics in the medical environment, unable to accurately identify and classify different types of interference sources (medical equipment interference and consumer electronics interference), and lack effective interference avoidance strategies and adaptive resource allocation mechanisms.

[0003] In view of this, this application proposes a communication system for a digital ward to solve the above problems. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art and to achieve the above objectives, this application provides the following technical solution: A communication system for a digital ward, comprising: The distributed acquisition module is used to acquire electromagnetic spectrum scanning data and initial communication link topology data at preset spatial grid sampling points within the ward area. The electromagnetic spectrum scanning data includes the power spectral density distribution of the received signal at each spatial grid sampling point in the target communication frequency band. The interference identification module is used to perform spectral energy gradient analysis on the power spectral density distribution of the received signal along adjacent spatial grid sampling points, identify spatial grid sampling point clusters whose energy accumulation at the same frequency exceeds a preset interference threshold, and mark them as interference accumulation areas; The scattering analysis module is used to transmit broadband probe signals at the boundaries of each interference accumulation area and collect channel impulse responses. Based on the channel impulse responses, the excess delay and root mean square delay spread are calculated, and a multipath scattering characteristic distribution model is constructed. The shielding identification module is used to calculate the signal attenuation gradient between adjacent spatial grid sampling points along the communication link propagation path in the initial communication link topology data, identify areas where the signal attenuation gradient exceeds the shielding identification threshold, and mark them as electromagnetic shielding transition areas. The channel map construction module is used to fuse interference accumulation areas, multipath scattering characteristic distribution models and electromagnetic shielding transition areas to construct a spatiotemporal channel availability map. The spatiotemporal channel availability map contains a set of available frequency-time slot resource blocks for each spatial grid sampling point. The communication scheduling module is used to generate frequency hopping time slot interleaving communication scheduling schemes for each communication link based on the spatiotemporal channel availability map. The relay bridging module is used to calculate the signal arrival angle distribution and select relay forwarding nodes at the spatial grid sampling points in the preset electromagnetic shielding transition area to establish a preventive relay bridging link. The link monitoring module is used to collect the real-time packet error rate and signal-to-noise ratio of each active communication link, compare them with the predicted values ​​in the spatiotemporal channel availability map, and calculate the channel state deviation metric. The adaptive update module is used to trigger incremental updates of the interference accumulation area and the electromagnetic shielding transition area based on the channel state deviation metric, and to regenerate the frequency hopping time slot interleaving communication scheduling scheme. The modules are connected via wired and / or wireless means to enable data transmission between them.

[0005] The technical effects and advantages of the communication system for a digital ward proposed in this application are as follows: This application enhances the reliability and adaptability of digital ward communication systems in complex electromagnetic environments, effectively addressing the unique communication challenges of medical settings. Through a precise interference identification mechanism, the system can distinguish between electromagnetic interference generated by medical and civilian equipment, achieving accurate location and feature extraction of interference sources, thereby reducing the impact of co-channel interference on medical data transmission. Addressing multipath scattering effects, the scattering feature distribution model employed in this application allows communication parameters to be dynamically adjusted according to the actual propagation environment, effectively suppressing link instability caused by signal fading and improving the continuity and anti-interference capability of medical data transmission. Regarding electromagnetic shielding areas, the boundary awareness and preventative bridging mechanism introduced in this application eliminates monitoring blind spots present in traditional communication systems, ensuring seamless maintenance of the communication link during patient movement and enhancing the comprehensiveness and safety of patient monitoring. The adaptive environmental awareness capability of this application enables it to quickly respond to dynamic changes in the hospital environment, performing local resource reallocation only in areas of significant change, significantly reducing system resource consumption while maintaining high responsiveness. Attached Figure Description

[0006] Figure 1 This is a schematic diagram of a communication system for a digital ward according to this application. Detailed Implementation

[0007] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0008] This application provides a communication system for a digital ward. The system's execution entities include, but are not limited to, devices such as medical communication management terminals, ward network controllers, medical device communication interfaces, and patient monitoring data acquisition terminals, which can be considered general communication nodes in this application. The communication system includes, but is not limited to, at least one of a distributed electromagnetic environment sensing engine, a dynamic spectrum resource management system, and an adaptive link optimization controller.

[0009] Please see Figure 1 In this embodiment of the application, a communication system for a digital ward includes: The distributed acquisition module is used to acquire electromagnetic spectrum scanning data and initial communication link topology data at preset spatial grid sampling points within the ward area. The electromagnetic spectrum scanning data includes the power spectral density distribution of the received signal in the target communication frequency band at each spatial grid sampling point, acquired in real time through a distributed radio frequency sensor network. The initial communication link topology data records basic information such as the connection relationships, relative positions, and initial link quality between communication terminals. The spatial grid sampling points are deployed according to the characteristics of the ward environment, increasing the sampling density in areas with dense medical equipment and appropriately reducing the density in open areas, with a typical sampling interval of 1-2 meters. Each sampling point is equipped with a small radio frequency sensing node, capable of high-precision scanning in the target frequency band (such as medical-specific bands, ISM bands, etc.), generating a power spectral density distribution map, accurately recording electromagnetic environment characteristics, and providing basic data for subsequent interference identification and channel optimization.

[0010] The interference identification module performs spectral energy gradient analysis on the power spectral density distribution of the received signal along adjacent spatial grid sampling points. It identifies clusters of spatial grid sampling points where the energy accumulation at the same frequency exceeds a preset interference threshold, marking them as interference clusters. Spectral energy gradient analysis identifies areas of abnormal energy distribution in space by calculating the spectral energy differences between adjacent sampling points. Energy accumulation at the same frequency manifests as a localized high value of signal power density in a specific frequency band, usually indicating the presence of an interference source. The preset interference threshold is dynamically set based on the communication quality requirements of medical devices, ensuring the identification of interference that substantially affects medical communication. The marking of interference clusters includes key parameters such as the region boundary coordinates, the dominant interference frequency band, and the energy accumulation level, providing precise spatial location for subsequent interference avoidance.

[0011] The scattering analysis module transmits broadband probe signals at the boundaries of various interference-concentrated areas and acquires the channel impulse response. Based on the channel impulse response, it calculates the excess delay and root-mean-square delay spread, constructing a multipath scattering characteristic distribution model. The broadband probe signal typically employs a pseudo-random sequence modulated broadband signal, possessing good autocorrelation characteristics, facilitating accurate estimation of multipath delay. The channel impulse response reflects the multipath effect of the signal during spatial propagation, including information such as arrival time, amplitude, and phase. The excess delay represents the additional propagation delay relative to the direct path, while the root-mean-square delay spread quantifies the temporal dispersion of the multipath components. The multipath scattering characteristic distribution model maps these parameters to a spatial coordinate system, constructing a spatial distribution map of scattering characteristics, providing a basis for identifying strong scattering regions and optimizing transmission parameters.

[0012] The shielding identification module calculates the signal attenuation gradient between adjacent spatial grid sampling points along the communication link propagation path in the initial communication link topology data, identifies regions where the signal attenuation gradient exceeds the shielding identification threshold, and marks them as electromagnetic shielding transition regions. Signal attenuation gradient analysis identifies electromagnetic shielding structures in space by comparing the signal strength changes of adjacent sampling points. In normal propagation environments, signal strength attenuates smoothly with increasing distance, while electromagnetic shielding causes a sharp drop in signal strength over short distances. The shielding identification threshold is set based on a free-space propagation model after correction, taking into account the shielding characteristics of common building materials and medical equipment in ward environments. The marking of electromagnetic shielding transition regions includes parameters such as the region center location, average attenuation gradient, and shielding strength, providing key constraint information for communication link design.

[0013] The channel map construction module fuses interference clustering regions, multipath scattering characteristic distribution models, and electromagnetic shielding transition regions to construct a spatiotemporal channel availability map. This map contains a set of available frequency-time slot resource blocks for each spatial grid sampling point. The spatiotemporal channel availability map is the core basis for optimal allocation of communication resources, comprehensively characterizing channel availability across spatial, frequency, and temporal dimensions. The map construction process transforms constraints such as interference, scattering, and shielding into constraints on resource block availability, calculating link quality indicators for each spatial point at different time slots and frequencies through multi-factor fusion. The set of available frequency-time slot resource blocks formally represents the available communication resources for each spatial point, including parameters such as center frequency, bandwidth, available time slots, and expected channel quality, providing a comprehensive decision-making basis for subsequent communication scheduling.

[0014] The communication scheduling module generates frequency-hopping and time-slot interleaving communication scheduling schemes for each communication link based on the spatiotemporal channel availability map. The scheduling scheme aims to maximize channel quality and minimize interference by dynamically allocating frequency and time-slot resources to optimize the configuration of communication links. Frequency-hopping and time-slot interleaving technology distributes communication across multiple frequencies and time slots, improving anti-interference capabilities and spectrum utilization efficiency. The scheduling process first prioritizes communication links according to service priority, ensuring that critical services such as vital sign monitoring receive optimal resources; then, it allocates appropriate frequency-time-slot combinations to each link, forming orthogonal frequency-hopping sequences to avoid co-channel interference; finally, it generates a complete scheduling table to guide the communication behavior of each terminal, achieving globally optimized resource allocation.

[0015] The relay bridging module calculates the signal angle of arrival (AHA) distribution at spatial grid sampling points within a pre-defined electromagnetic shielding transition zone and selects relay nodes to establish a preventative relay bridging link. AHA analysis utilizes multi-antenna array technology to accurately measure the spatial directional characteristics of the signal. Relay node selection considers various factors such as node location, communication capabilities, and link quality to ensure effective bridging of communication terminals on both sides of the shielded area. The preventative relay bridging link is pre-established before communication interruption, enabling seamless switching when a terminal enters the shielded area, avoiding communication disruption. The bridging process employs intelligent routing technology, automatically selecting the optimal transmission path to ensure communication continuity and reliability, making it particularly suitable for scenarios where medical personnel or patients are moving with equipment in ward areas.

[0016] The link monitoring module collects real-time packet error rate (PER) and signal-to-noise ratio (SNR) data for each active communication link, compares these values ​​with predicted values ​​in the spatiotemporal channel availability map, and calculates a channel state deviation metric. PER and SNR are core indicators for evaluating link quality, directly reflecting the reliability and effectiveness of communication. Real-time monitoring is achieved through a built-in quality feedback mechanism in the communication protocol, periodically collecting performance data for each link. Predicted value comparison compares measured data with predicted values ​​from the map, calculates the relative deviation, and quantifies the accuracy of the prediction and the degree of environmental change. The channel state deviation metric serves as the basis for adaptive triggering; when the deviation exceeds a preset threshold, local environmental re-sensing and parameter adjustment are initiated to ensure the system's timely response to environmental changes.

[0017] The adaptive update module triggers incremental updates to the interference accumulation area and electromagnetic shielding transition area based on channel state deviation metrics, and regenerates the frequency-hopping time-slot interleaving communication scheduling scheme. The incremental update strategy only rescans and analyzes areas with significant deviations, rather than reconstructing the entire system, improving system response speed and resource utilization efficiency. The update process includes re-performing electromagnetic spectrum scanning, updating the boundaries of the interference accumulation area and electromagnetic shielding transition area, and locally adjusting the spatiotemporal channel availability map. The scheduling scheme regeneration only applies to affected communication links, maintaining the stability of other links while ensuring optimized global resource allocation. This adaptive mechanism enables the system to cope with dynamic changes in the hospital environment, such as equipment switching, personnel movement, and temporary interference sources, maintaining the continuous and efficient operation of the communication system.

[0018] The modules are connected via wired and / or wireless means to enable data transmission between them.

[0019] In this embodiment of the application, the detailed implementation steps of performing spectral energy gradient analysis on the power spectral density distribution of the received signal along adjacent spatial grid sampling points, identifying spatial grid sampling point clusters whose co-frequency energy accumulation exceeds a preset interference threshold, and marking them as interference accumulation regions include: Frequency-band energy accumulation is performed on the received signal power spectral density distribution at each spatial grid sampling point. The target communication frequency band is divided into multiple sub-bands, and the energy value of each sub-band is calculated separately, forming the frequency band energy vector for that sampling point. Frequency-band energy accumulation is a fundamental step in spectrum analysis, improving the accuracy and specificity of interference identification through frequency band segmentation. Target communication frequency bands (such as the 2.4 GHz ISM band) are typically divided into several sub-bands (such as 20 MHz wide sub-bands), and the energy value of each sub-band is calculated independently. Energy accumulation uses the power spectral density integration method, summing the signal power density within a specific frequency range to form a discrete frequency band energy vector. This frequency-band processing method can more accurately identify frequency-selective interference and provides finer-grained spectral characteristics than broadband energy analysis.

[0020] The rate of change of the Euclidean distance between adjacent sampling points in the spatial grid is calculated and denoted as the spectral spatial gradient. The spectral spatial gradient is a key indicator for quantifying changes in spectral energy distribution in space; it reflects the spatial variation characteristics of energy distribution by calculating the energy vector distance between adjacent sampling points. The calculation process first calculates the Euclidean distance for each pair of adjacent sampling points: ; in, Sampling points and The Euclidean distance between them and The two sampling points are respectively at the th Energy value of each sub-band This represents the total number of sub-bands.

[0021] Then calculate the rate of change of distance relative to spatial location, i.e., the spatial gradient of the spectrum: ; in, For the spectral spatial gradient, Sampling points and The physical distance between them. A higher spectral gradient value indicates a more drastic change in energy distribution, possibly indicating the presence of interference source boundaries; a lower value indicates a more uniform energy distribution, belonging to a homogeneous region.

[0022] Connected regions with a spectral spatial gradient less than a preset uniformity threshold and energy values ​​in each sub-band exceeding a preset interference threshold are marked as interference clusters. Interference cluster identification comprehensively considers both spatial uniformity and energy thresholds. Uniformity assessment ensures the continuity of spectral characteristics within the region, indicating the influence range of the same interference source; energy threshold assessment ensures that the identified interference truly impacts communication. Connected regions are determined using the connected component algorithm in graph theory, grouping adjacent sampling points that meet both conditions into the same region. Each interference cluster is assigned a unique identifier, and its boundary coordinates, coverage area, average energy level, and other characteristic parameters are recorded, forming a structured description of the interference region.

[0023] Principal component analysis (PCA) was performed on the frequency band energy vectors within each interference cluster region to extract the dominant interference frequency band. Based on the occupied bandwidth and burst cycle characteristics of the dominant interference frequency band, the interference sources were classified into medical equipment interference sources or civilian equipment interference sources. PCA is an effective tool for dimensionality reduction and feature extraction; its application to frequency band energy vectors can identify the main energy distribution patterns. The analysis process first constructs a frequency band energy matrix for all sampling points within the interference region, with each row representing a sampling point and each column representing a sub-frequency band. Then, the covariance matrix is ​​calculated, and its eigenvalues ​​and eigenvectors are solved. Finally, the dominant interference frequency band is determined through eigenvector projection. The interference source classification is based on the characteristic parameters of the dominant frequency band. Medical equipment interference sources typically exhibit continuous occupation of specific frequency bands and relatively regular burst cycles, while civilian equipment interference sources (such as Wi-Fi and Bluetooth devices) exhibit bandwidth occupation and irregular activity patterns. This classification provides a targeted basis for subsequent interference avoidance. Medical equipment interference usually requires frequency isolation solutions, while civilian equipment interference can be mitigated through protocol negotiation or scheduling optimization.

[0024] In this embodiment, the detailed implementation steps for transmitting broadband probe signals and acquiring channel impulse responses at the boundaries of each interference aggregation region, calculating excess delay and root mean square delay spread based on the channel impulse responses, and constructing a multipath scattering characteristic distribution model include: At the boundary spatial grid sampling points of each interference cluster region, pseudo-random sequence broadband sounding signals are transmitted at a preset period. Pseudo-random sequence broadband sounding signals are an effective tool for measuring channel characteristics, exhibiting good autocorrelation and spectral coverage. The sounding signals typically use maximum-length sequences (M-sequences) or Gold sequences, generated as broadband signals through spread spectrum modulation to ensure accurate detection of multipath channels. The transmission period is set according to the rate of environmental change, typically 1-5 minutes in a medical environment, balancing timeliness and system load. The selection of boundary sampling points is based on the contour analysis of the interference cluster region, ensuring comprehensive acquisition of the electromagnetic characteristics of the region boundaries. Sounding signal parameters (such as bandwidth, power, and sequence length) are dynamically adjusted according to measurement accuracy requirements and medical environment limitations, ensuring high-precision measurement without interfering with the normal operation of medical equipment.

[0025] The channel impulse response of a broadband probe signal is acquired at the communication receiver, and the arrival delay and corresponding amplitude of each multipath component are extracted. Channel impulse response acquisition is achieved through a correlation receiver, which performs correlation calculations between the received signal and a locally generated reference sequence to obtain the time-domain impulse response. Multipath component extraction employs a peak detection algorithm to identify significant peaks in the impulse response; each peak represents a propagation path. The arrival delay is determined by the peak position, and the amplitude is quantized by the peak height; these two parameters together describe the basic characteristics of the multipath components. In a medical environment, the typical number of multipath components is 5-15, reflecting the rich scattering environment caused by complex indoor structures. Adaptive thresholding technology is used during multipath extraction to ensure extraction stability under different signal-to-noise ratio conditions, while anti-interference processing is applied to reduce the impact of electromagnetic interference from medical equipment.

[0026] Using the arrival delay of the direct path as a benchmark, the excess delay of each multipath component is calculated, and the root mean square delay spread is calculated based on the amplitude weights of each multipath component. Excess delay and root mean square delay spread are two core parameters for quantifying the temporal dispersion characteristics of a multipath channel. The formula for calculating excess delay is: ; in, For the first Excess delay of multiple path components, For its absolute arrival delay, This is the arrival time delay of the direct trajectory.

[0027] The root mean square delay spread calculation takes into account the relative intensity of each multipath component, and the formula is: ; in, For root mean square delay spread, For the first Power (amplitude squared) of each multipath component. This represents the total number of multipath components.

[0028] The root mean square delay spread directly affects the maximum lossless transmission rate of a communication system and is a key basis for designing communication parameters. The larger the value, the more severe the scattering, requiring a longer guard interval or a lower symbol rate.

[0029] A multipath scattering characteristic distribution model is constructed using the root mean square time delay spread as a feature quantity and the spatial grid sampling point positions as spatial coordinates. Regions where the root mean square time delay spread exceeds a preset time delay threshold are designated as strong multipath scattering regions. The multipath scattering characteristic distribution model is a spatial mapping of scattering characteristics, extending the scattering parameters of discrete measurement points to continuous space. The model construction employs Kriging interpolation, a statistical method that can consider spatial correlations to provide optimal linear unbiased estimation. ; in, Points to be estimated The root mean square time delay spread estimate at [location] For known sampling points The measured value at that location, The weighting coefficient is determined by spatial correlation.

[0030] After interpolation generates a complete spatial distribution of scattering features, threshold segmentation is applied to identify strong multipath scattering regions. The threshold is set based on the system bandwidth and modulation scheme, typically 10%-20% of the symbol period. Strong multipath scattering regions require special handling in communication planning, such as employing equalization techniques, diversity reception, or reducing the modulation order, to ensure reliable communication in high-scattering environments.

[0031] In this embodiment of the application, the detailed implementation steps of calculating the signal attenuation gradient between adjacent spatial grid sampling points along the communication link propagation path in the initial communication link topology data, identifying regions where the signal attenuation gradient exceeds the shielding identification threshold, and marking them as electromagnetic shielding transition regions include: The total received signal power is extracted pairwise from adjacent spatial grid sampling points along the propagation path of each communication link. Total received signal power is a fundamental indicator for evaluating link quality, directly reflecting path loss and shielding effects during signal propagation. The extraction process is based on initial communication link topology data, linearly sampling along the theoretical propagation path, and calculating the total received power of the target frequency band for each sampling point. Considering the complexity of communication links in medical environments, the sampling interval is typically set to 0.5-1 meter to ensure that rapidly changing shielding boundaries are captured. The total power calculation uses a spectral integration method, accumulating the power spectral density within a specific frequency band to form a single power indicator. For complex environments with multiple potential paths, several main propagation paths are examined simultaneously to ensure that critical shielding areas are not overlooked.

[0032] The ratio of the difference in total received signal power between adjacent spatial grid sampling points to the sampling point spacing is calculated and denoted as the signal attenuation gradient. The signal attenuation gradient is a key indicator for quantifying the rate of change of signal strength in space. Normalization eliminates the influence of the sampling spacing, allowing direct comparison of gradient values ​​from different paths. The calculation formula is: ; in, For signal attenuation gradient, and These represent the total power of the received signals at two adjacent sampling points (in dBm). The physical distance (in meters) between two points.

[0033] In a normal propagation environment, the signal attenuation gradient follows the expected value of free space or a specific indoor propagation model, typically 1-4 dB / m; however, electromagnetic shielding structures cause the gradient value to increase sharply, forming local high gradient regions, which is a key feature for identifying shielded areas.

[0034] A sliding window extreme value detection method is used to identify spatial intervals where the signal attenuation gradient sequence along the same propagation path continuously exceeds the shielding identification threshold. Sliding window extreme value detection is an effective method for detecting outliers in a sequence and is suitable for identifying gradient abrupt changes at shielding boundaries. The detection process uses a fixed-width sliding window (usually 3-5 sampling points), which moves gradually along the propagation path. Statistical tests are applied to the gradient values ​​within the window to identify intervals that significantly deviate from the background level. The shielding identification threshold is set by comprehensively considering the environmental background gradient, measurement error, and shielding identification requirements, and is typically set to 3-5 times the normal gradient. Spatial intervals continuously exceeding the threshold indicate the existence of sustained abnormal attenuation, rather than temporary fluctuations or measurement errors, and are a typical characteristic of shielding structures. Extreme value detection employs adaptive threshold technology, automatically adjusting the decision criteria according to the local environment to improve the robustness of identification.

[0035] The spatial interval is extended to both sides along the propagation path by one sampling interval to form an electromagnetic shielding transition region. The center position coordinates and average attenuation gradient value of each electromagnetic shielding transition region are recorded. The extension process takes into account the gradual characteristics of the shielding boundary and the limitations of measurement accuracy. By expanding the identification interval, the shielding structure is fully captured. The electromagnetic shielding transition region represents the transition zone from normal propagation to strong shielding, which is usually a key area that needs special attention in communication link design. The center position coordinates are calculated using a weighted average method, with the gradient value as the weight, highlighting the contribution of high attenuation points; the average attenuation gradient directly quantifies the shielding strength and is an important reference for subsequent relay deployment and link planning. Each shielding transition region is assigned a unique identifier and associated with physical structures in the medical environment (such as X-ray room walls, operating room shielding, equipment cabinets, etc.) to form a semantically rich description of the shielding environment.

[0036] In this embodiment of the application, the detailed implementation steps for constructing a spatiotemporal channel availability map by integrating the interference accumulation region, the multipath scattering characteristic distribution model, and the electromagnetic shielding transition region include: For each spatial grid sampling point, the dominant interference frequency band information of the interference clustering region to which the sampling point belongs is extracted. The root mean square delay spread value corresponding to the sampling point is extracted from the multipath scattering characteristic distribution model, and it is determined whether the sampling point is located within the electromagnetic shielding transition region. Multi-source information extraction is the first step in constructing a comprehensive channel map, integrating dispersed environmental features into a unified spatial reference system. The dominant interference frequency band information includes key parameters such as interference frequency range, interference intensity, and time occupancy rate, which directly affect the availability of frequency resources. The root mean square delay spread value reflects the multipath scattering intensity, affecting the selection of signal modulation and coding schemes. Shielding region determination examines the spatial relationship between the sampling point and the identified shielding transition region to determine the constraints on signal propagation. The data extraction process uses spatial indexing technology to accelerate queries. For sampling points located at the boundaries of multiple feature regions, fuzzy classification methods are applied to determine their main attributes, ensuring the coherence and rationality of feature extraction.

[0037] Interfering sub-bands are excluded based on the dominant interference frequency band information. The maximum usable symbol rate for each sampling point is determined based on the root mean square delay spread. If the sampling point is located within an electromagnetic shielding transition area, the corresponding shielding attenuation is marked. Resource constraint analysis is a crucial step in transforming environmental characteristics into communication resource constraints, determining the communication conditions for each sampling point through multi-dimensional evaluation. Interfering sub-band exclusion uses spectrum masking technology based on interference characteristics to mark interfering frequency bands and their guard bandwidths as unusable resources. The formula for calculating the maximum usable symbol rate is: ; in, For the maximum available symbol rate, This is the root mean square delay spread value. This is a system-dependent constant, determined according to the modulation scheme and bit error rate requirements, and is typically 0.1-0.2.

[0038] The shielding attenuation is extracted directly from the characteristics of the electromagnetic shielding transition region and serves as an additional attenuation term in the link budget, affecting the selection of transmission power and modulation scheme. These three constraint dimensions together define the communication capability boundary of the sampling point, providing a quantitative basis for resource allocation.

[0039] Based on the combined exclusion results, maximum available symbol rate, and shielding attenuation, a set of available frequency-time slot resource blocks for the sampling point is generated. Resource block generation is the implementation step of mapping multi-dimensional constraints to specific communication resources, forming operable resource allocation units through discretization. In the frequency dimension, the available spectrum is divided into fixed-bandwidth subcarriers or channels (such as a 20MHz WLAN channel), eliminating interfered sub-bands; in the time dimension, time slot resources are divided according to the system frame structure, considering the time patterns of interference; in the rate dimension, the modulation and coding scheme and data capacity of each resource block are determined based on the maximum available symbol rate. For sampling points located in the shielding transition region, additional power compensation needs to be calculated to ensure link reliability. The final resource block set generated for each sampling point takes the form of… ,in Indicates frequency, Indicates time slot, These represent modulation and coding schemes, which together define the communication resource space available for each sampling point.

[0040] The available frequency-time slot resource blocks of all spatial grid sampling points are organized according to spatial location to form a spatiotemporal channel availability map. Map integration is the final step in extending the resource information of discrete sampling points to continuous space, generating a comprehensive channel model covering the entire environment. The integration process uses three-dimensional interpolation technology to construct a continuous model in the spatial, frequency, and time dimensions, enabling resource query capabilities at any point. Kriging interpolation is used in the spatial dimension to ensure that spatial correlation is considered; piecewise linear interpolation is used in the frequency dimension to maintain the locality of spectral characteristics; and a periodic model is used in the time dimension to capture the temporal patterns of interference. The final generated map is a multi-dimensional data cube containing channel availability information for every spatial location, every frequency point, and every time point in the ward environment, providing a comprehensive and detailed environmental awareness foundation for communication scheduling, and enabling precise resource allocation and link optimization in the medical environment.

[0041] In this embodiment of the application, the detailed implementation steps for generating a frequency hopping time slot interleaving communication scheduling scheme for each communication link based on the spatiotemporal channel availability map include: The available frequency-time slot resource block sets of the spatial grid sampling points where the transmitter and receiver of each communication link are located are extracted from the spatiotemporal channel availability map. The intersection of these sets is then used to obtain the common available resource block set of the communication link. Link resource matching is the first step in generating the scheduling scheme. By finding the intersection of the resource sets of the transmitter and receiver, the actual available communication resources of the link are determined. The extraction process first determines the spatial location of the transmitter and receiver based on the communication link topology data, then queries the resource block set at the corresponding location from the spatiotemporal channel availability map, and finally calculates the intersection of the two sets. ; in, A set of resource blocks that are commonly available for the link. and These are the sets of available resource blocks for the transmitter and receiver, respectively.

[0042] Intersection calculation ensures that the selected resources are available at both ends of the link, which is a necessary condition for successful communication. If the intersection is empty, the system will trigger a relay path search, using multi-hop transmission to address the problem of insufficient direct-connection resources.

[0043] Resource blocks with the best channel quality are allocated to vital sign monitoring links based on their data priority. Priority scheduling is a key feature of medical communication systems, ensuring that critical data is protected during resource contention. The prioritization process determines the priority level based on service type and clinical importance: vital sign monitoring (such as ECG and blood oxygenation) has the highest priority, followed by medical image transmission, and then management data and environmental control. The channel quality score for resource blocks comprehensively considers signal-to-noise ratio, bandwidth, and stability. ; in, Score the quality of resource blocks. For the expected signal-to-noise ratio, For available bandwidth, is the coefficient of variation for time stability.

[0044] High-priority links prioritize high-scoring resource blocks to ensure the transmission quality and reliability of critical medical data. This is a core characteristic that distinguishes communication systems in medical settings from those in ordinary settings.

[0045] For different communication links allocated to the same time slot, their frequency hopping sequences are constrained to maintain the minimum frequency interval in the frequency domain, generating orthogonal frequency hopping sequence groups. Orthogonal frequency hopping sequences are an effective technique for reducing co-channel interference, ensuring that multiple links transmitting simultaneously do not interfere with each other through frequency domain separation. Sequence generation first calculates the number of concurrent links to determine the granularity of frequency resource allocation; then, available sub-frequency bands are grouped according to the minimum frequency interval principle, and each group is allocated to one communication link; finally, a cyclic frequency hopping sequence is generated for each link, hopping within the allocated sub-frequency band set. Orthogonality constraints ensure that at any given time, the frequencies used by different links are sufficiently far apart, reducing mutual interference and improving the overall system capacity. For high-density deployment scenarios, a coloring algorithm is also used for spatial reuse optimization to maximize the spatial reuse efficiency of frequency resources.

[0046] The orthogonal frequency-hopping sequence groups of each communication link are combined with the allocated time slot numbers to form a frequency-hopping time slot interleaving communication scheduling scheme. Time slot interleaving is a scheduling technique that distributes transmission in the time dimension, further enhancing anti-interference capabilities. The scheduling scheme generation integrates resource allocation in both frequency and time dimensions to form a complete communication guidance scheme. The scheduling information for each link includes: link identifier, active time slot set, frequency-hopping sequence corresponding to each time slot, modulation and coding scheme, and power control parameters. This information is organized in a compact data structure and sent to each communication terminal through the control channel to guide its communication behavior. Time slot allocation adopts a distributed strategy, distributing the transmission of each link across multiple discontinuous time slots to improve fault tolerance to sudden interference. The overall scheduling scheme is optimized through linear programming to maximize system throughput while meeting the service quality requirements of each link, achieving globally optimal resource allocation.

[0047] In this embodiment of the application, the detailed implementation steps for calculating the signal arrival angle distribution and selecting relay forwarding nodes at the spatial grid sampling points in the preset electromagnetic shielding transition area, and establishing a preventive relay bridging link, include: In the electromagnetic shielding transition area, the nearest spatial grid sampling points are selected on both sides, denoted as the outer sampling point and the inner sampling point, respectively. The selection of sampling points on both sides is a fundamental step in relay bridging, determining the two key nodes that the relay system needs to connect. The selection process is based on spatial distance calculations, searching for the nearest valid sampling points from the shielding area boundary outwards to both sides. The outer sampling point is typically located in an area with good signal coverage, maintaining a reliable connection with the base station or main communication nodes; the inner sampling point is located inside the shielding area, in an area with severe signal attenuation but still requiring communication coverage. The sampling point selection considers signal quality, location stability, and spatial representativeness, ensuring that the selected points effectively represent the communication environment on both sides. For long, narrow, or large shielding areas, the system selects multiple pairs of sampling points and deploys multiple relay nodes at key locations along the shielding boundary to form a complete coverage network.

[0048] At the outer sampling point, a multi-antenna array is used to measure the signal angle of arrival (AoA) from the base station, while at the inner sampling point, the strongest azimuth angle of the receivable signal is measured. AoA measurement is a crucial step in determining the optimal relay location, optimizing relay deployment through spatial signal characteristic analysis. The measurement process involves deploying a multi-antenna array at the outer sampling point and accurately measuring the signal AoA distribution using phase difference calculation or beam scanning technology; at the inner sampling point, a directional antenna is used for rotating scanning to identify the strongest direction of the receivable signal. The AoA calculation employs the MUSIC algorithm or phase correlation method to achieve high-precision angle estimation. ; in, For MUSIC spectral functions, It is a direction vector. The noise subspace matrix, This is the corresponding conjugate transpose.

[0049] The direction angle measurement results directly indicate the main path of signal propagation, providing spatial guidance for the optimal location of the relay node and ensuring that the relay can effectively bridge the signal transmission on both sides.

[0050] Relay nodes are deployed at locations capable of establishing communication paths with both outer and inner sampling points. Relay node selection is a core step in establishing bridging links, determining the deployment location of physical relay equipment. The selection process is based on geometric analysis and signal propagation prediction, seeking the optimal location within a spatial area that satisfies bidirectional communication conditions. Ideally, the relay location should be near the intersection of the outer angle of arrival and the inner strongest azimuth angle, while also considering constraints of the actual deployment environment, such as available installation points, power supply, and physical security. Relay nodes typically employ a dual-band, dual-antenna architecture, using different frequency bands for reception and transmission to avoid self-interference; a time-division multiplexing scheme can also be used, receiving and transmitting in different time slots. After deployment, node testing is conducted to verify the link quality with both sampling points, and position adjustments are made as necessary to optimize overall performance. In complex environments, multi-level relays or distributed relay networks may be required to form a complete coverage solution.

[0051] A backup communication link is pre-established between the relay node and the sampling points on both sides. When a communication terminal is detected entering the electromagnetic shielding transition area, the backup communication link is triggered to switch to the active relay bridging link. Preventative link establishment is a key strategy to improve seamless handover, enabling rapid response through advance preparation. Backup link establishment includes three aspects: resource pre-allocation, link parameter pre-setting, and control channel preparation, ensuring minimal delay during handover. Resource pre-allocation reserves dedicated resource blocks for the relay link in the spatiotemporal channel availability map to avoid resource contention; link parameter pre-setting includes modulation and coding schemes, power control parameters, and synchronization information, reducing parameter negotiation during handover; control channel preparation ensures that handover commands can still be sent even if the data link is interrupted. Terminal entry detection employs a dual judgment mechanism based on signal strength and location awareness. When the terminal's received signal strength drops below a threshold or location tracking shows it is approaching the shielded area, the handover process is triggered. The handover process uses soft handover technology, establishing a relay link before disconnecting the direct link to ensure communication continuity, which is particularly important for critical services such as medical monitoring. The entire preventative relay mechanism provides reliable protection for mobile communications in hospital environments, overcoming the coverage challenges posed by electromagnetic shielding.

[0052] In this embodiment of the application, the detailed implementation steps for collecting the real-time packet error rate and signal-to-noise ratio of each active communication link, comparing them with the predicted values ​​in the spatiotemporal channel availability map, and calculating the channel state deviation metric include: The system collects packet error rate (PER) and signal-to-noise ratio (SNR) data for each active communication link at a preset statistical period to form measured channel quality indicators. Real-time channel monitoring is a fundamental step in closed-loop optimization, continuously collecting performance data to assess the current communication status. The data collection process is integrated into the feedback mechanism of the communication protocol. Communication terminals periodically report key indicators such as PER and SNR, with a typical statistical period of 100-300 milliseconds, dynamically adjusted according to the rate of environmental change. The PER is calculated through CRC check failure statistics, reflecting the link's error performance; the SNR is estimated from the received signal strength and noise floor, reflecting the link's signal quality. These two indicators complement each other, together forming a comprehensive measure of link quality. The data collection employs a sliding window averaging technique to reduce the impact of instantaneous fluctuations and improve the representativeness and stability of the indicators. For high-priority links, the system also collects more detailed performance indicators, such as bit error rate and latency jitter, providing a more accurate assessment of link status.

[0053] The system queries the spatiotemporal channel availability map to find the predicted channel quality index (CQI) corresponding to the current spatial grid sampling point and the current frequency of the communication link. This predicted value query is a crucial step in establishing a comparison benchmark, providing theoretical reference values ​​through model prediction. The query process first determines the current spatial locations of both ends of the link, and then extracts the predicted CQI for the corresponding location, frequency, and time slot from the spatiotemporal channel availability map. For mobile terminals, the system uses location tracking technology to update their coordinates in real time to ensure query accuracy; for fixed terminals, preset location information is used. The predicted CQI includes the expected packet error rate (PBER) and expected signal-to-noise ratio (SNR), which are theoretical estimates derived from environmental feature analysis during map construction. The query is accelerated using multidimensional indexing technology, supporting rapid access to large-scale maps and providing an efficient foundation for real-time comparison. The predicted values ​​represent the system's perception of the current environment; deviation analysis reveals the gap between this perception and the actual situation, guiding subsequent adaptive adjustments.

[0054] The relative deviation between the measured channel quality index and the predicted channel quality index is calculated and denoted as the channel state deviation metric. Deviation calculation is a core step in quantifying environmental changes, revealing the difference between system perception and the actual environment through numerical comparison. The calculation employs a normalized relative deviation formula to eliminate the influence of different dimensions of the indices, resulting in a unified deviation metric. ; in, As a measure of channel state deviation, and These are the measured and predicted packet error rates, respectively. and These are the signal-to-noise ratios for measurement and prediction, respectively. To prevent small constants from being divided by zero.

[0055] The deviation metric comprehensively considers the differences in both packet error rate and signal-to-noise ratio, providing a comprehensive quantitative indicator of link state changes. For links where the deviation metric exceeds a preset threshold, the system marks them as "significantly changed environment," triggering a subsequent adaptive update process to ensure that the system's perception is synchronized with the actual environment and to guarantee communication quality.

[0056] In this embodiment of the application, the detailed implementation steps for triggering incremental updates of the interference accumulation region and the electromagnetic shielding transition region based on the channel state deviation metric, and regenerating the frequency hopping time slot interleaving communication scheduling scheme include: Communication links whose channel state deviation exceeds a preset deviation threshold are identified, and their corresponding spatial grid sampling points and adjacent sampling points are marked as regions to be updated. Abnormal region identification is the starting point for incremental updates, improving update efficiency through targeted analysis. The identification process first sets a deviation threshold (typically 0.3-0.5, adjusted according to system stability requirements), marking links exceeding the threshold as abnormal links; then, it determines the spatial sampling points traversed by these links, forming an initial update candidate set; finally, a spatial expansion algorithm is applied to expand the candidate set to adjacent sampling points, forming a complete region to be updated. The expansion range is typically 1-2 sampling point distances, ensuring the complete boundary of environmental changes is captured. This targeted update strategy significantly reduces system burden, reducing computational and communication overhead by 70%-90% compared to global rescanning, while maintaining sensitive response to critical changes, making it an efficient adaptive mechanism for resource-constrained systems.

[0057] Electromagnetic spectrum scanning is re-performed on the spatial grid sampling points within the area to be updated to obtain the updated received signal power spectral density distribution. Area rescanning is a core step in environmental re-sensing, updating the system's understanding with fresh data. The rescanning process performs the same electromagnetic spectrum scanning procedure as the initial scan on all sampling points within the area to be updated, obtaining the current signal power spectral density distribution. To improve efficiency, a smart sampling strategy is employed, adjusting the scanning frequency and precision based on the degree of anomaly. Points with significant deviations are scanned at high density, while the sampling density is appropriately reduced in edge regions. The scan results undergo data cleaning and feature extraction to generate an updated spectral feature representation, providing foundational data for subsequent analysis. During the rescanning process, the system maintains normal communication, performing scans during communication gaps through time-division multiplexing or using a dedicated monitoring channel for parallel acquisition, ensuring a balance between service continuity and real-time monitoring.

[0058] Based on the updated received signal power spectral density distribution, the spectral spatial gradient and signal attenuation gradient within the region to be updated are recalculated, adjusting the boundary ranges of the interference accumulation region and the electromagnetic shielding transition region. Boundary recalculation is a crucial step in updating the environmental model, correcting the definitions of interference and shielding regions using the latest data. The calculation process reuses the aforementioned interference and shielding identification algorithms, recalculating the spectral spatial gradient and signal attenuation gradient based on the updated power spectral density distribution, and identifying regions exceeding corresponding thresholds. The newly identified interference accumulation regions and electromagnetic shielding transition regions are compared with the original regions, and the degree of change is assessed using the region intersection-over-union (IoU) ratio. ; in, For intersection, union, and comparison, For the original area, For the new area, Indicates the area of ​​the region.

[0059] For regions with an intersection-over-union ratio (IoU) below a threshold (typically 0.7), the system updates its boundary definition, recording the direction and magnitude of boundary changes as historical data on the dynamic characteristics of the environment, which helps in future predictions. Boundary adjustment directly affects the accuracy of the channel availability map and is a key step in ensuring communication quality.

[0060] The system reconstructs the available frequency-time slot resource block set in the spatiotemporal channel availability map only for areas where the boundary range has changed, and regenerates frequency-hopping time slot interleaving communication scheduling schemes for the affected communication links. Incremental updates are an efficient mechanism for the system to respond to environmental changes, minimizing resource consumption through local reconstruction. The update process first determines the spatial range of the boundary-changed area, then re-executes the channel map construction process only for these areas to generate an updated set of available resource blocks; next, it identifies the affected communication links, including existing links crossing the updated area and potentially newly built links; finally, it re-executes scheduling optimization for these links to generate updated frequency-hopping time slot interleaving schemes. This incremental update strategy significantly improves the system's response speed; in typical scenarios, the complete update cycle can be controlled within 0.5-2 seconds, meeting the stringent requirements for communication stability in medical environments. During the update process, the system adopts a smooth transition mechanism to ensure service continuity, employing a dual-path transmission strategy for critical medical data links, maintaining both the old and new schemes simultaneously before switching to eliminate switching risks.

[0061] In this embodiment of the application, the detailed implementation steps for generating orthogonal frequency hopping sequence groups by constraining the frequency hopping sequences of different communication links allocated to the same time slot to maintain the minimum frequency interval in the frequency domain include: The number of communication links allocated to the same time slot is recorded as the concurrent link count. Concurrent link analysis is the foundation of frequency resource allocation, determining resource allocation strategies through link statistics. The analysis process first extracts the set of active links for a specific time slot from the global scheduling table and calculates the total number of links transmitting simultaneously; then, it groups the links according to their priority and spatial distribution, identifying subsets of links that may interfere with each other; finally, it determines the number of concurrent links within each subset, serving as the basis for frequency allocation. In a medical environment, the typical number of concurrent links is 5-20, varying depending on ward density and equipment deployment. The number of concurrent links directly affects the granularity of frequency resource allocation; the higher the number, the fewer frequency resources are available per link, and the stricter the constraints on frequency hopping design, requiring more refined frequency planning and more stringent orthogonality control.

[0062] The available sub-frequency bands in the set of commonly available resource blocks within the time slot are sorted by frequency. The ratio of the total number of available sub-frequency bands to the number of concurrent links is calculated and rounded down, and this ratio is recorded as the minimum frequency interval step size. Frequency interval calculation is a crucial step in ensuring link orthogonality, and frequency isolation is ensured through mathematical partitioning. The calculation process first organizes all available sub-frequency bands in the time slot, removes interfering frequency bands, and forms a list of available frequencies; then, the interval step size is calculated based on the number of concurrent links to ensure that each link has at least one available frequency. ; in, The minimum frequency interval step size, This represents the total number of available sub-bands. For the number of concurrent links, This indicates rounding down to the nearest integer.

[0063] Setting the frequency spacing step size takes into account both orthogonality requirements and system capacity, making it a key parameter for efficient use of spectrum resources. In areas with complex interference environments, the system will appropriately increase the step size to improve frequency isolation; in areas with less interference, the step size can be decreased to improve spectrum utilization.

[0064] Each concurrent communication link is assigned a starting sub-band index, with the starting index spacing between adjacent links not less than the minimum frequency interval step size. Starting band allocation is the initialization step in frequency hopping sequence construction, ensuring full orthogonality by staggering the starting points. The allocation process first sorts the links by priority, with higher-priority links selecting the optimal frequency band first; then, starting indices are assigned sequentially from low to high frequency, ensuring that the interval between adjacent links meets the step size requirement. ; in, For the first The starting sub-band index of the link. Counting starts from 0. The minimum frequency interval step size, This represents the total number of available sub-bands.

[0065] The proper distribution of starting indices is the fundamental guarantee of the orthogonality of frequency hopping sequences, ensuring that even in the worst case, the frequencies of different links can maintain minimum isolation, reduce mutual interference, and improve communication reliability.

[0066] Each communication link maintains the constraint of minimum frequency interval step size in subsequent frequency hopping steps, cyclically traversing its assigned sub-band subset to form an orthogonal frequency hopping sequence group. Frequency hopping sequence generation is the core algorithm of the scheduling scheme, enhancing anti-interference capability through carefully designed frequency switching patterns. The generation process constructs a complete frequency hopping sequence for each link; the sequence length is determined according to the communication cycle, typically 32-256 hops. Each link's sequence starts from its assigned starting index and cyclically hops within its dedicated frequency band subset according to predetermined step rules: ; in, For the first The link is in the 1st The sub-band index used per frequency hopping cycle. For link The step value is usually set to a coprime odd number to ensure the maximum cycle time.

[0067] To enhance security and anti-interference capabilities, the system also introduces pseudo-random perturbations, superimposing controllable random variations on the basic rules to generate more unpredictable frequency hopping patterns. The resulting orthogonal frequency hopping sequence group maintains the minimum frequency interval at any given time, achieving link isolation in the frequency domain. This significantly improves communication reliability in interference environments, providing a solid guarantee for medical data transmission.

[0068] This application achieves intelligent communication management in a digital ward environment through distributed data acquisition, interference identification, scattering analysis, shielding identification, channel map construction, communication scheduling, relay bridging, link monitoring, and adaptive updates. The spatiotemporal channel sensing method of this application can accurately identify the electromagnetic characteristics in the medical environment, effectively avoid interference and shielding effects, provide reliable protection for medical data transmission, and has significant value for improving the communication quality of the Internet of Things in healthcare.

[0069] The above are merely preferred embodiments of this application and are not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0070] It should be noted that all formulas in this manual are calculated by removing dimensions and taking their numerical values. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0071] Although embodiments of this application have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the claims and their equivalents.

Claims

1. A communication system for a digital ward, characterized in that, include: The distributed acquisition module is used to acquire electromagnetic spectrum scanning data and initial communication link topology data at preset spatial grid sampling points in the ward area. The electromagnetic spectrum scanning data includes the power spectral density distribution of the received signal at each spatial grid sampling point in the target communication frequency band. The interference identification module is used to perform spectral energy gradient analysis on the power spectral density distribution of the received signal along adjacent spatial grid sampling points, identify spatial grid sampling point clusters whose energy accumulation at the same frequency exceeds a preset interference threshold, and mark them as interference accumulation areas; The scattering analysis module is used to transmit broadband probe signals at the boundaries of each of the interference accumulation regions and collect channel impulse responses. Based on the channel impulse responses, the excess delay and root mean square delay spread are calculated, and a multipath scattering characteristic distribution model is constructed. The shielding identification module is used to calculate the signal attenuation gradient between adjacent spatial grid sampling points along the communication link propagation path in the initial communication link topology data, identify the region where the signal attenuation gradient exceeds the shielding identification threshold, and mark it as an electromagnetic shielding transition region. The channel map construction module is used to fuse the interference accumulation region, the multipath scattering feature distribution model and the electromagnetic shielding transition region to construct a spatiotemporal channel availability map. The communication scheduling module is used to generate a frequency hopping time slot interleaving communication scheduling scheme for each communication link based on the spatiotemporal channel availability map. The relay bridging module is used to calculate the signal arrival angle distribution and select relay forwarding nodes at the spatial grid sampling points in the preset electromagnetic shielding transition area to establish a preventive relay bridging link. The link monitoring module is used to collect the real-time packet error rate and signal-to-noise ratio of each active communication link, compare them with the predicted values ​​in the spatiotemporal channel availability map, and calculate the channel state deviation metric. An adaptive update module is used to trigger incremental updates of the interference accumulation area and the electromagnetic shielding transition area based on the channel state deviation metric, and to regenerate the frequency hopping time slot interleaving communication scheduling scheme.

2. The system according to claim 1, characterized in that, The spectral energy gradient analysis of the power spectral density distribution of the received signal along adjacent spatial grid sampling points identifies spatial grid sampling point clusters whose energy accumulation at the same frequency exceeds a preset interference threshold, and marks them as interference accumulation regions, including: The power spectral density distribution of the received signal at each spatial grid sampling point is accumulated by frequency band, the target communication frequency band is divided into multiple sub-frequency bands and the energy value of each sub-frequency band is calculated to form the frequency band energy vector of the sampling point. Calculate the rate of change of the Euclidean distance between sampling points of adjacent spatial grids in the frequency band, denoted as the spectral spatial gradient; The connected regions where the spectral spatial gradient is less than a preset uniformity threshold and the energy values ​​of each sub-band exceed the preset interference threshold are marked as the interference clustering regions; Principal component analysis is performed on the frequency band energy vectors in each of the aforementioned interference cluster regions to extract the dominant interference frequency bands. Based on the occupied bandwidth and burst period characteristics of the dominant interference frequency bands, the interference sources are classified as medical equipment interference sources or civilian equipment interference sources.

3. The system according to claim 1, characterized in that, The process of transmitting broadband probe signals at the boundaries of each interference aggregation region and acquiring channel impulse responses, calculating excess delay and root mean square delay spread based on the channel impulse responses, and constructing a multipath scattering characteristic distribution model includes: At the boundary space grid sampling points of each of the aforementioned interference aggregation regions, a pseudo-random sequence broadband detection signal is transmitted at a preset period. The channel impulse response of the broadband probe signal is acquired at the communication receiving end, and the arrival delay and corresponding amplitude of each multipath component are extracted. Based on the arrival delay of the direct path, the excess delay of each multipath component is calculated, and the root mean square delay spread is calculated by weighting the amplitude of each multipath component. Using the root mean square time delay spread as a feature quantity and the spatial grid sampling point position as spatial coordinates, a multipath scattering feature distribution model is constructed, and the region in the model where the root mean square time delay spread exceeds a preset time delay threshold is marked as a strong multipath scattering region.

4. The system according to claim 1, characterized in that, The process of calculating the signal attenuation gradient between adjacent spatial grid sampling points along the communication link propagation path in the initial communication link topology data, identifying regions where the signal attenuation gradient exceeds the shielding identification threshold, and marking them as electromagnetic shielding transition regions includes: The total power of the received signal from adjacent spatial grid sampling points is extracted pairwise along the propagation path of each communication link; The ratio of the difference in total received signal power between adjacent spatial grid sampling points to the sampling point spacing is calculated and denoted as the signal attenuation gradient. A sliding window extreme value detection is performed on the signal attenuation gradient sequence along the same propagation path to identify the spatial interval in which the signal attenuation gradient continuously exceeds the shielding identification threshold. The spatial interval is extended to both sides along the propagation path by a sampling interval to form the electromagnetic shielding transition region, and the center position coordinates and average attenuation gradient value of each electromagnetic shielding transition region are recorded.

5. The system according to claim 1, characterized in that, The process of fusing the interference aggregation region, the multipath scattering characteristic distribution model, and the electromagnetic shielding transition region to construct a spatiotemporal channel availability map includes: For each spatial grid sampling point, extract the dominant interference frequency band information of the interference clustering region to which the sampling point belongs, extract the root mean square time delay spread value corresponding to the sampling point from the multipath scattering feature distribution model, and determine whether the sampling point is located in the electromagnetic shielding transition region. Based on the dominant interference frequency band information, the interfered sub-frequency bands are excluded, and the maximum usable symbol rate of the sampling point is determined based on the root mean square delay spread value. If the sampling point is located in the electromagnetic shielding transition area, the corresponding shielding attenuation is marked. Based on the combined exclusion results, the maximum available symbol rate, and the shielding attenuation, a set of available frequency-slot resource blocks for this sampling point is generated. The available frequency-time slot resource blocks of all spatial grid sampling points are organized according to spatial location to form the spatiotemporal channel availability map.

6. The system according to claim 1, characterized in that, The step of generating a frequency hopping time slot interleaving communication scheduling scheme for each communication link based on the spatiotemporal channel availability map includes: Extract the available frequency-time slot resource block set of the spatial grid sampling points where the transmitter and receiver of each communication link are located from the spatiotemporal channel availability map, and take the intersection to obtain the common available resource block set of the communication link; Based on the data priority of each communication link, the resource block with the best channel quality is allocated first to the vital signs monitoring link; For different communication links allocated to the same time slot, constrain their frequency hopping sequences to maintain the minimum frequency interval in the frequency domain to generate orthogonal frequency hopping sequence groups; The orthogonal frequency hopping sequence groups of each communication link are combined with the allocated time slot numbers to form the frequency hopping time slot interleaving communication scheduling scheme.

7. The system according to claim 1, characterized in that, At the spatial grid sampling points in the preset electromagnetic shielding transition area, the signal arrival angle distribution is calculated and relay forwarding nodes are selected to establish a preventive relay bridging link, including: The nearest spatial grid sampling points are selected on both sides of the electromagnetic shielding transition region and are respectively denoted as the outer sampling point and the inner sampling point. The angle of arrival of the signal from the base station is measured at the outer sampling point using a multi-antenna array, and the strongest direction angle of the receivable signal is measured at the inner sampling point. Select node locations that can simultaneously establish communication paths with both the outer and inner sampling points, and deploy the relay forwarding nodes thereon. A backup communication link is pre-established between the relay forwarding node and the sampling points on both sides. When the communication terminal is detected to enter the electromagnetic shielding transition area, the backup communication link is triggered to switch to the active relay bridging link.

8. The system according to claim 1, characterized in that, The real-time packet error rate and signal-to-noise ratio of each active communication link are collected and compared with the predicted values ​​in the spatiotemporal channel availability map to calculate the channel state deviation metric, including: Packet error rate and signal-to-noise ratio of each active communication link are collected at a preset statistical period to form measured channel quality indicators; Query the predicted channel quality index corresponding to the current spatial grid sampling point and the current frequency of the communication link from the spatiotemporal channel availability map; The relative deviation between the measured channel quality index and the predicted channel quality index is calculated and denoted as the channel state deviation metric.

9. The system according to claim 1, characterized in that, The step of triggering incremental updates to the interference accumulation region and the electromagnetic shielding transition region based on the channel state deviation metric, and regenerating the frequency hopping time slot interleaving communication scheduling scheme, includes: Identify communication links whose channel state deviation metric exceeds a preset deviation threshold, and mark their spatial grid sampling points and adjacent sampling points as areas to be updated; The electromagnetic spectrum scan is re-performed on the spatial grid sampling points in the area to be updated to obtain the updated power spectral density distribution of the received signal. Based on the updated received signal power spectral density distribution, the spectral spatial gradient and signal attenuation gradient within the region to be updated are recalculated, and the boundary range between the interference accumulation region and the electromagnetic shielding transition region is adjusted. Only for regions where the boundary range changes, the set of available frequency-time slot resource blocks corresponding to the spatiotemporal channel availability map is reconstructed, and the frequency hopping time slot interleaving communication scheduling scheme is regenerated for the affected communication links.

10. The system according to claim 6, characterized in that, The step of constraining different communication links allocated to the same time slot to maintain a minimum frequency interval in the frequency domain for their frequency hopping sequences, and generating an orthogonal frequency hopping sequence group, includes: The number of communication links allocated to the same time slot is counted and recorded as the number of concurrent links; The available sub-frequency bands in the set of common available resource blocks within the time slot are sorted by frequency. The ratio of the total number of available sub-frequency bands to the number of concurrent links is calculated and rounded down, and recorded as the minimum frequency interval step size. Assign a starting sub-band index to each concurrent communication link, and the starting index spacing between adjacent links shall not be less than the minimum frequency interval step size; Each communication link maintains the constraint of the minimum frequency interval step size in subsequent frequency hopping steps, and iterates through its assigned sub-frequency band subsets to form the orthogonal frequency hopping sequence group.

Images